Paxillin is essential for PTP-PEST-dependent regulation of cell spreading and motility: a role for paxillin kinase linker
Bibliographic record
Abstract
The tyrosine phosphatase PTP-PEST has been implicated in the regulation of cell spreading and migration through dephosphorylation of focal adhesion proteins and inhibition of Rac GTPase activity. The focal adhesion adaptor protein paxillin is also necessary for normal cell migration and binds directly to PTP-PEST. In this study, we have utilized PTP-PEST(-/-) and paxillin(-/-) fibroblasts to demonstrate that paxillin is essential for PTP-PEST inhibition of cell spreading and membrane protrusion as well as inhibition of adhesion-induced Rac activation. Furthermore, we show that paxillin-binding is necessary for PTP-PEST stimulation of cell migration. Mutation analysis indicates that PTP-PEST function involves binding to the paxillin C-terminal LIM domains, and signaling through the tyrosine 31 and 118 phosphorylation sites, as well as the LD4 motif of the paxillin N-terminus. Using 'substrate trapping' approaches and immunoprecipitation, we show that the ARF GAP paxillin kinase linker PKL/GIT2, a paxillin LD4 binding partner, is a substrate for PTP-PEST. Additionally, the PKL-paxillin interaction was necessary for PTP-PEST inhibition of cell spreading. These data provide mechanistic insight into how the paxillin-PTP-PEST interaction contributes to integrin signaling events associated with the spatiotemporal regulation of key modulators of the cytoskeleton and cell motility machinery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".